accenture digitizing energy analytics powered performance

Digitizing Energy

Analytics-Powered PerformanceOpportunities for oil and gas companies to improve business outcomes

2Introduction 3

Section 1: The current analytics landscape in 5 oil and gas

1.1 Survey reveals barriers to better business outcomes 6

1.2 Insights into the analytics tipping point 11

Section 2: Seizing the opportunities for 16 analytics-powered performance

2.1 Upstream operations 18

2.2 Downstream operations 23

2.3 Corporate functions 24

Section 3: Achieving better business outcomes 28 from analytics in oil and gas

3.1 Accenture analytics capability maturity model 29

3.2 Recommendations for becoming analytically-powered 31

Visualize the value and design for analytics outcomes 32

Adopt an end-to-end process view, integrating enterprise 33 and operations analytics

Promote a cultural shift to an analytically astute, insight-driven 35 enterprise

Conclusion 37

Contents

Analytics

Insights

Action

3

Energy is an industry dominated by engineers and scientists, working in disciplines that embrace facts and figures to reach conclusions and make informed decisions. Yet a recent Accenture survey of analytics practitioners indicates that only one in five energy-industry respondents report using an integrated, organization-wide approach to analytics.1

One of the industrys greatest challenges is data integration (perceived as a greater barrier in oil and gas than in other industries surveyed), with additional challenges including functional silos and cultural resistance to changeall of which inhibit an integrated approach to end-to-end processes.

With the rise of big data and a wide range of new technologies, analytics have reached the tipping point. More than three-quarters of survey respondents in oil and gas indicate their senior leadership is highly or totally committed to fact-based decision making. But will companies be able to turn this commitment into a healthy return on investment?

This report explores the barriers blocking companies from generating improved outcomes from analytics, whether in upstream or downstream sectors, and in corporate functions. It considers areas where companies should consider focusing near-term investments, and provides case studies of how organizations have leveraged analytics for tangible gains. It also offers a road map to gauge maturity on the journey to becoming a business where analytics drive competitive essence.

Oil and gas companies that want to use analytics to power their businesses will start by visualizing the value and designing for improved outcomes. Analytics can be used to improve exploration and drilling, especially related to unconventionals. Downstream businesses can apply analytics for greater insights into logistics and supply chain, marketing and tradingto better manage operations end to end, from the demand side to commercial channels. The leaders will promote a cultural shift, fostering an analytically astute, insight-driven enterprise that relies on data-driven decision making. Thinking outside the box, they will integrate new technologies

digital, mobile, cloud and analyticsnot only to improve current activities, but also to design dramatically improved workflows aligned with high performance.

High performance hinges on the ability to gain insights from data-insights that enable organizations to make better decisions at the right time.

Introduction

Cost effective outcomes

Analytics

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5Section 1 The current analytics landscape in oil and gasEnergy companies have been measuring and monitoring production for years, using sensors and machine-to-machine communications. Many tools to boost performance, however, were not designed for analytical optimization. In addition, ad-hoc adoption has led to bolt-on tools and standalone analytics, which have not been integrated with the overall enterprise architecture.

There is skepticism about analytics, which is understandable. Consider the digital oil field, a concept that emerged some 15 years ago. Digital oil field is a suite of interactive and complementary technologies for operators, business partners and service companies to combine data and knowledge management, using enhanced analytical tools to develop more efficient business processes and make timely decisions. The promise of the digital oil field has, however, not been fully delivered, with hurdles ranging from a lack of sponsorship across functions, to weak workflow integration and data ownership, to technology limitations.

Additionally, the quantity and variety of data vary greatly with asset age. Older facilities lack instrumentation, thereby limiting advanced analytics. Conversely, thanks to the sensors and automation in newer facilities, torrents of data are being produced, but few companies have worked out how to turn growing amounts of data into insights yielding improved profitability.

To better understand progress to date, Accenture surveyed 35 analytics practitioners in oil and gas companies as part of a global research effort interviewing 600 executives in total across multiple industries.2 The survey addressed questions such as:

What are the key challenges with analyzing data?

How committed is senior leadership to adoption of analytics?

How widely are analytics used across the organization?

What are the overall levels of satisfaction with outcomes from analytics?

61.1 Survey reveals barriers to better business outcomesSurvey responses show that energy companies have started to mobilize to embrace some of the practices necessary to achieve greater return on investment with analytics: recruiting the right talent, investing in tools and software, and evolving mindsets toward fact-based decision making. Most energy companies surveyed are demonstrating commitment to analytics in multiple ways.

Survey findings, however, reveal that much more needs to be done to realize improved, analytics-driven outcomes. From the survey, and also from working with energy companies throughout the world, Accenture sees an integration gap, or missing middle, as the key barrier to better business outcomes from analytics in energy.

Numerous signs of the missing middle are evident from survey responses relating to lack of integration of data and systems, functions and workflow, and corporate strategies and culture.

The missing middle in energy companiesIn many companies, a breachthe missing middleis evident in multiple dimensions: between the data available and disparate systems used; from the lack of end-to-end integration across processes or workflows; and between corporate strategies and analytics efforts at functional and departmental levels.

With this gap, energy companies struggle for a complete and timely assessment of the impact of operational decisions on corporate performance. Likewise, corporate entities are unable to factor in day-to-day field operations in their objective setting and planning decisions.

Source: Accenture Analytics Adoption Study, March 2013.

Poor data quality and lack of integration

Data collected is oftentimes not relevant to the business

No single, holistic data strategy, resulting in fragmented use of analytics

Limited visibility of data across the breadth of processes

Patchy ownership of data across processes

C-suite does not lead by example to mandate insight-based decisions

Figure 1. Summary of key challenges with analytics in oil and gas companies.

7Figure 2. Please select what are your key challenges when it comes to analyzing data?

Global Energy

Data integration

Outcome from data

Data collection

Data interpretation and approach

Identifying insights from data (e.g., industry data models)

Software and applications investment (e.g., virtualization)

Talent acquisition

Base: Total Energy Respondents (n=35)Global Total (n=600)


34%47%

43%49%

49%49%

49%42%

54%48%

60%58%

63%50%

Integration of data and systems The energy industry has some way to go to possess the data quality necessary for sophisticated analytics. That is to say, to secure insight through analytics using real-time data across multiple assets and spanning a range of activities, including operations and back-office functions.

As indicated by responses to survey question 23 [Q23], data integration is the greatest challenge relative to data quality and the ability to analyze data (see Figure 2). Sixty-three percent of energy respondents (significantly higher than 50 percent for the other industries overall) perceive data integration as a barrier. Responses to additional data-related survey questions indicate that more than one-half of energy respondents cite format, completeness and accessibility as issues. Accenture attributes problems such as these to a lack of data governance, which results in disparate systems and redundancy.

One-half of respondents identified their current technological resources and systems as one of the top two inhibitors to the wider use of analytics. [Q21] The technology hurdle is, however, not likely due to lack of investment; less than one-half of the respondents cited software and applications investments as a challenge [Q23].

Energy companies have relatively formal structures for data related to enterprise departments, such as finance, largely because of the enterprise resource planning (ERP) backbone defining the related activities. In general, however, the data collected is lacking not only in quality, but there is also a disconnect between what is collected and its relevance to the business; only 40 percent see their data as relevant [Q3]. And yet, nearly 70 percent of respondents [Q5] indicate that unique, proprietary data is of value to differentiate their companies, a slightly higher percentage than the overall level across all industries.

There is a data disconnect between what is collected and its relevance to the business; only 40 percent of respondents see their data as relevant.

8Figure 3. In which, if any, of the following business functions (areas) are you currently setting targets based on analytics? (Q14b)

Global Energy

Quicker/more effective decision making

Improved efficiency/productivity

Market performance/sales

Competitive performance/market share

New concept/product development

Cost savings



40%

40%

54%

63%

74%

48%

48%

47%

59%

61%

77%62%

Functions and workflowNot surprisingly in this asset-intensive industry, three-quarters of energy respondents confirm that their use of analytics is primarily focused on generating insights and setting targets for quicker, more effective decision making (77 percent), as well as improving efficiency and productivity (74 percent) (see Figure 3).

In addition, nearly two-thirds of respondents continue to manage analytics by specific function or department, which inhibits an integrated approach for end-to-end workflows. Accenture sees this finding as further evidence of the missing middle.

Accenture experience shows that the missing middle persists, in large part, due to a lack of data ownership across the breadth of processes. While the CIO and back-office activities at headquarters might have worked together to help achieve corporate goals related to improved data management, reporting and analytics, the efforts in the field, in particular, have remained ad-hoc and siloed. Attempts to bridge gaps and improve integration are often limited because of the complexity and lack of prioritization.

Additionally, there has been a lack of focus on analytics related to the core activity of production. Only one out of two of the executives interviewed said their organizations were using sophisticated analytics related to their operations, compared to more than two-thirds of respondents using analytics in finance and customer management [Q1a].3

Rather than taking an organization-wide approach, 60 percent of respondents are managing software and systems at a departmental level.

9Corporate strategies and cultureMost oil and gas companies are running global portfolios, complex and interconnected operations across which they take steps to confirm a company-wide analytics strategy. While 60 percent of the respondents say data is currently lacking in relevance, the good news is that 71 percent of energy respondents (significantly more than the global sample, 62 percent) confirmed that the C-suite is demonstrating a commitment to a data strategy by establishing exactly what data needs to be collected, shared and how (see Figure 4).

The findings show strong leadership support for fact-based decision making: 77 percent are highly or totally committed. The commitment is reinforced by the appointment of a chief data officer at 60 percent of energy companies surveyed, of which 40 percent had recruited the role within the past 12 months [Q10a/b].

Nevertheless, part of the missing middle is the need not only to collect and analyze the data, but also to enable the workforce to act on the insight. Analytics should not, after all, be intended primarily for a data officer and his or her corporate team. Only 40 percent of respondents confirmed that becoming more analytical in decision-making styles and methods across the enterprise was a long-term goal. Indeed, only 29 percent of middle managers get all of the training needed to use analytics effectively in day-to-day decision making [Q21].

Only one in five energy companies attests to routine use of analytics as part of an integrated, enterprise-wide approach ingrained into the fabric of the company.

Only 29 percent of middle managers get all of the training needed to use analytics effectively in day-to-day decision making.

Figure 4. How has the C-suite demonstrated a commitment to a data strategy for your firm? (Q12)

Global Energy

Establishing what data needs to be shared and how, and what needs to be collected and how

Identifying the companys data inventory and how it is being catalogued

Defining a data strategy plan, with a clear owner, that has been communicated across the organization

Ensuring a truly integrated data strategy with suppliers, service providers and other partners with the focus on data: specifying which data each party can access, who owns what, how it is to be used and shared

Making a commitment to source top data talent to address a data-skills gap; determining where the gaps are greatest and giving ownership to the right teams to fill them

Putting someone in charge of reviewing the concept of data exchanges through which data can be shared internally and maybe even bought and sold with external partners

Giving ownership to others to map out ways to ratchet up expectations about how to better analyze data and how to better report on it



40%50%

34%50%

46%49%

49%50%

49%50%

51%49%

By asking teams to evaluate data, to not only describe what is happening within the company, but also to predict trends 63%

60%

71%62%

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Figure 5. When thinking about your companys use of analytics, please state if used broadly across the organization, in specific functions or not at all.

Across the organization Specific function areas of the company Not at all

Analytics management

Energy results

Analytical software/systems

Corporate data

Analysts/analytical talent



34% 60%

31% 58% 11%

31% 60% 9%

6%

37% 52% 11%

As long as analytics management, systems, data and talent are managed at functional levels, the missing middle is destined to remain [Q7] (see Figure 5). Bridging the gaps will require the full integration of corporate and financial objectives with the operational and enterprise analytics driven from a field level.

As additional input to this report, Accenture commissioned a separate crowd-sourcing survey, conducted by 10EQS, of energy executives with analytics responsibilities and experience. This survey inquired about pain points, opportunities and barriers to analytics adoption. Some of the quotations from executives (who remain anonymous as part of the 10EQS survey methodology) are highlighted in this report.

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Figure 6. Four major technology developments are creating a tipping point, with new possibilities to enable faster, smarter business decisions.

1. Convergence of information technology (IT) and operations technology (OT) Data from machines is generated, captured and integrated into IT systems for analysis, often in real time.

2. Mobility and technology consumerizationMobility and user-friendly IT provide remote access to data, affording improved visibility and faster response.

3. Disruptive architectures, including cloud computing Cloud provides cost-effective options to access large-scale computing and storage capability, as well as tools, all essential for analytics. Also, "on demand" is reshaping the IT operations model with the option for analytics-as-a-service.

4. Big data analytics and in-memory computingThe application of advanced analytic techniques to very large data sets is aided by in-memory databases that greatly reduce query time.

Source: Accenture analysis, 2013.

1.2 Insights into the analytics tipping pointThe macro trends affecting the industry are widely known: growing energy demand, rising capital costs, global competition, increasing regulation and compliance. To address these issues, oil and gas executives are focused on boosting production and return on investment, while managing safety and risk.

At the same time, a range of technology-based, digital developments, are coming into play, with oil and gas companies now much more actively assessing how they might significantly improve production and meet their safety and risk goals. For more than a decade, the vision for these four technologies has been shared, but only recently has the technology attained the maturity to underpin a genuine transformation (see Figure 6).

These technology trends are gaining momentum concurrently, which amplifies their impact and ability to transform. Digital, mobile, cloud and analytics technologies are converging, producing the potential for dramatically improved business outcomes.

Each of these digital trends is (by definition) related to data: how it is generated, captured, integrated, managed, analyzed and stored. In this context, oil and gas companies will digitize processes through these technologies, and will start to compete on data management and analytical proficiency as has been the case in other industries. This development potentially will change how energy leaders run operations, manage people and meet customer needs.

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2. Mobility and the consumerization of IT

Trend Advances in wireless technology mean that consistent, wireless connections in industrial landscapes can be achieved. The landscape of intrinsically safe mobility is about to change with the introduction of I-Safe tablet devices and new options for Wi-Fi connectivity. Technology innovation in mobility is further extending uses (e.g., drones, intelligent-vision eyewear).

Also, as a result of consumer-oriented, hand-held technologies, oil and gas workersin enterprise functions and operationswant highly intuitive applications facilitating the collection, use, treatment and sharing of data related to their work. Investment in mobility platforms inevitably will lead to wider access to real-time data and analysis.

Related terms Bring your own device; radio frequency identification (RFID); track and trace; embedded mobility (M2M); mobile as the new financial wallet.

Relevance to oil and gas

Highly relevant to industries with geographically dispersed, or with field-located assets and workforces, to provide access to performance data. Also relevant to support mass customer interactions.

Figure 7. A detailed look at technology trends contributing to the analytics tipping point in oil and gas companies.4

1. Convergence of information technology and operations technology (IT/OT)

Trend The gap (to date characterized by different systems, standards and manufacturers) between operations and information technologies can be bridged, enabling the integration of data from sensors and devices, with control systems (SCADA), middleware (e.g., manufacturing execution systems) and back-end IT systems (e.g., ERP). Data from machines can now be captured and integrated into IT systems for analysisfrequently in real time.

Related terms Machine-to-machine (M2M) communication; industrial internet; sensors and meters.


Directly relevant to asset-intensive industries, especially where assets are geographically distributed.

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What about social? Nearly 80 percent of US smartphone users check social media within 15 minutes of waking up. As consumer technologies come to the workplace, social networking is reshaping how companies interact with customers and third parties. However, the impact in oil and gas is not yet as significant as with trends such as cloud, mobility and big data. Energy companies are likely to consider opportunities with social networking and analytics related to seamless collaboration between employees and customers, as well as potentially even devices. In downstream, for example, the monitoring of social media postings can enable the evaluation of market sentiment, including potential opportunities, brand health and competitor information. Likewise, in upstream, companies may monitor postings to assess positive and negative sentiment related to their environmental or their health and safety record.

3. Disruptive architectures, including cloud computing

Trend The on-demand dynamic is gaining momentum in the oil and gas industry. This development affects the IT operations model in data center operations and enables flexible sourcing. Most companies are moving to capture efficiencies from procuring server and data storage capacity as a service. Also, off-premise modelssuch as software as a service and analytics as a serviceenable companies to outsource certain technology-related activities, while focusing on what they do best.

Relevant terms Cloud computing: private, public, hybrid, community; software-as-a-service (SaaS); platform-as-a-service (PaaS); infrastructure-as-a-service (IaaS).


Increasingly relevant to oil and gas companies as a means to harness the computing power required to manage massive data volumes, at reasonable cost.

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4. Big data analytics and in-memory computingTrend Big data describes the exponential growth, availability and use

of information, which is diverse in type and not necessarily structured. Data volumes have multiplied in all industries, including oil and gas.

Big data analytics is the application of advanced analytic techniques to very large data sets. It is not longer necessary to centralize big data because data representation architecture is being decoupled from applications.

As critical data volumes increase, the speed of computing becomes vital. Open-source tools make it possible to make sense of huge amounts of data, automating the integration of traditionally structured and unstructured data. Advances in extract-transfer-load (ETL) tools ease the process of gathering and integrating information from disparate sources for statistical analysis and modeling. The openness of Internet protocols further supports organization-wide analytics. Most importantly, compared to traditional disk storage, in-memory data greatly reduces query time, providing for faster and more predictable performance, reducing processing time from hours to seconds.

Relevant terms Data management; business intelligence; analytics; in-memory database.


Opportunities for companies generating large volumes of complex data from multiple sources (from instrumented machinery to global positioning signals, text messages, social network postings and Internet searches).

The insight gained from analytics applied to big data enables oil and gas companies to improve core operations and business functions. In the future, energy companies that are leaders in analytics-heavy activities could start to sell their capabilities to peers on a pay-per-use basis.

The convergence of multiple technologies has led to the tipping point for analytics. Consequently, people in oil and gas businesses are on the cusp of using tools to make faster, smarter business decisions.

Source: Accenture Analytics, 2013.

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Figure 8. Impact of digital technologies (mobility, analytics, cloud, social) on key oil and gas processes.

Social CloudAnalyticsMobility

1

Planning & Enterprise Management

Support Services

Enterprise Strategy &Performance Management

Enterprise Risk Management Stakeholder Relationship MgmtPortfolio Management

Business & CompetitiveIntelligence

Finance & Accounts

Human Resources &Knowledge Mgmt

RegulatoryCompliance

InformationTechnology

PhysicalInfrastructure

Data & InformationManagement

Business ProcessLifecycle Mgmt

Ope

ratio

nal B

usin

ess

Business Value of Impact

Medium LowHigh None

Hydrocarbon Accounting, Planning & Forecasting

Non-Hydrocarbon Supply Chain Management

Supply Chain Strategy

RequirementsPlanning

Requisition to PaySourcing &Procurement

Upstream

Commercial Management

Exploration & Appraisal

Integrated Activity Planning

Contract & Nomination ManagementTrading & Risk Management

Joint Venture Management

Environment, Health, Safety (EH&S)

Decommissioning

Production Forecasting & Planning

Integrity Management

Hydrocarbon Accounting

Integrated Business Planning

Channel, Network & Offer Management

Operation Support

Research &Development

Product Supply Chain Management

Storage & MeteringBulk Movement & Transportation

Transportation & DistributionTerminal Operations

Process Engineering & Automation

Management of Change Laboratory, Chemical & Quality Mgmt

Construction & Project, Facilities Engineering

Warehousing &Logistics

Downstream


Accenture process model for energy

Development / Capital Projects

Field Production & Asset Operation Plant Production & Asset Operation

Drilling & Completion Marketing Operations (B2B, B2C/Retail)

Digital technology trends such as social, mobile, and cloud, are creating the tipping point for greater analytics adoption. None of these digital trends, including analytics, are a solution in of themselves to a business issue or opportunity. Nevertheless, when applied to meet a business objective, they can help increase efficiency and engagement by optimizing processes,

thereby enabling new ways of interacting with all the actors of the energy industry (customers, suppliers, employees, partners, regulators ...). Digital technologies will impact all parts of the industry value chain, particularly core process areas in upstream and downstream.

IssueStep 1

Step 2

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Section 2 Seizing the opportunities for analytics-powered performance With the emergence of the analytics tipping point, where should oil and gas executives focus investment? Companies perceive some of the likely benefits, but which areas are likely to provide the greatest returns?

In todays environment, where instant response is expected, oil and gas companies need advanced systems that enable them to do the things they already do but faster and with enhanced flexibility. Furthermore, advanced analytics solutions are needed to manage the volume of data (structured and unstructured), in upstream exploration and production (E&P), downstream and in corporate functions.

Step 2

Step 3Step 4

Outcome

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2.1 Upstream operationsOil and gas companies have learned how to predict the performance of conventional oil and gas wells. But unconventional wells perform unpredictably. Extracting more oil and gas from reserves remains a universal concern, and the efficient drilling of hundreds of unconventional wells is vital for profitability.

Analytics for drillingComplex algorithms and immense computing power are helping to interpret some of the largest data sets ever assembled, thereby aiding exploration of fields locked in shale formations and beneath the ocean floor. Powerful tools enable analysis of multiple data sets, and interpretive software and visualization tools make the information accessible to a wider internal audience.

Seismic data can be gigabytes in size, but this information is not what is typically thought of as big data, which tends to be transaction-based and can include unstructured data, such as social media postings. Unstructured data is less critical in E&P.

Analytics provide E&P companies with a better understanding of what is happening in reservoirs, modeling the way fluids move through rock formations and for optimal well placement. Analytics-based approaches might help to extract another three to five percent, meaning extra cash flow, which is important in high-priced commodity environments.

A largely untapped area for energy companies is analytics across assets, particularly in unconventional fields. Companies are starting to look at all the wells a company has drilled and, in addition to applying first principles, incorporating statistical data for insight into which wells are similar to the ones about to be drilled.5

Figure 9. Industry and corporate pain points where analytics can potentially drive better business outcomes.

Upstream operations Downstream operations Corporate operations

Forecast and deliver production commitments

Efficiently deliver unconventional plays

Enforce rational and appropriate working standards

Manage equipment supply chain

Execute capital projects to time, budget and specified scope.

Optimize end-to-end the integrated value chainfrom plant to pump

Configure the supply chain to enable cost reduction in the manufacture of specialty lubricants

Measure and manage market riskat commercial and logistics levels

Enforce rational and appropriate working standards.

Optimize cash flow to effectively meet planned capital expenditure commitments

Enable or manage contingent labor

Measure and manage market riskat a commercial level

Enforce rational and appropriate working standards

Execute capital projects to time, budget and specified scope.

Analytics-powered approaches could help companies identify likely upsets in drilling and production before they occur. One day of a shutdown could mean avoiding an estimated loss of $1 million per day in total costs. A largely untapped area is analytics across assets, particularly in unconventional fields.

Source: Accenture Analytics, March 2013.

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Conducting statistical analysis on data within a company is relatively straightforward, although the analytics processes might be complex. An unexplored opportunity for companies is to enlarge the sample of data with information from other companies operating in the same location. Analysis enabled by new visualization tools could enable scientists to see formerly unseen patterns and perhaps improve production yields by several percentage points.

Sources of data are limited not only to the drilling process, but also to non-technical information, for example, how long it takes to move a rig, or which rig is more effective under which conditions. Analytics can enable monitoring of operations across a basin, rather than from the more limited perspective of isolated wells, thereby reducing down-time and increasing speed to production. Greater insight from analytics also could lead to more efficient batch drilling.

Improving information sharing and collaboration across companiesE&P projects typically involve operators who hold onto their data rather than share it. It has been scientifically demonstrated, however, that greater pooling of data can yield benefits for multiple parties.

The success of statistical analysis depends on the size and quality of available data. With more diverse data sources, the industry could develop a clearer picture of ways to improve drilling in unconventional fields.

Companies have an immediate opportunity to learn more from their own internal data on E&P projects. Combining this data with similar information on wells drilled by other firms nearby (or in similar geological formations) would very likely yield greater insights, leading to improved outcomes.

The greater sharing of information would likely result in insights fueling continuous E&P improvement. While pooling of drilling data might seem anti-competitive in some circles, an argument could be made that improved practices are in the public interest as well as the industry as a whole, given continuing worldwide demand for energy.

A less controversial application of the cross-company collaborative approach might involve pooling energy industry data to gain insight into ways to improve health, safety, security and environmental practices.

Case in pointBuilding the foundation for a high-performing analytics company

This company is a privately-held oil and gas company in North America. As total production volume declined from roughly 250,000 barrels of oil a day in the 1980s to approximately 130,000 barrels a day, advanced analytics were seen as a way to bend the production curve.

The company possessed enormous amounts of databoth real time and historicalbut tools and systems were spreadsheet-based and the results localized. Company leaders sought new ways to use analytics to drive greater benefits. The company has developed an analytics program to build a foundation for an analytically-astute company. They are implementing SASs Predictive Asset Maintenance and Visual Analytics platforms, and using the

SAS tools on two test casesreducing subsurface impairment by optimizing water injection and increasing well production by optimizing rod pump set points. In addition to the technical mandate of integrating SAS tools and demonstrating their value to the company, this project will build enterprise-wide features of an analytic operating modeldefining the companys analytics vision, proposing a way to organize analytics talent, detailing business process implications, and developing a talent development and acquisition plan. The company is adopting a broader, more holistic approach to analytics that moves beyond limited point solutions to innovative ways to resolve business issues, organize talent and enhance processes.

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With rising capital costs related to E&P, the use of digital technology and advanced analytics is likely to increase. Interest is growing in using analytics to anticipate risk, be that from a preventive maintenance vantage point, or from health, safety and environmental perspectives.

Analytics for predictive asset maintenanceThe convergence of operations technology (OT) and IT is improving maintenance, enabling remote monitoring of equipment and planned (rather than unexpected) shutdowns. Indecision about the reliability of a drilling rig, for example, has a significant impact on cash flow.

In addition to using innovative technology tools, companies need to close the loop with associated processes. Asset-maintenance insights need to be communicated to multiple parties and workflows need to be integrated. Equipment and spare parts will be more readily available, for example, through greater sharing of information in near real time. Analytics related to asset maintenance also affect other processes, as the Taleris case study illustrates (see sidebar), influencing outcomes not only for capital assets, but also for scheduling and human resource utilization.

Case in point: TalerisRecovering faster from unplanned events, airlines get a lift from big data analytics

Companies in other asset-intensive industries already are achieving substantial results from advanced analytics. In the airline industry, for example, protracted delays undermine profitability, and analytics-powered solutions enable companies to recover faster and improve maintenance to avoid equipment breakdowns.

New technologies, for example, enable scanning of data from tip to tail on parts, components and systems. As Norm Baker, president and CEO of Taleris, a GE-Accenture joint venture, explains, Significant benefits can be realized through predictive analytics technologies, which leverage an aircrafts data within the context of the operations so one can address an issue before it occurs.

Etihad Airways, the national airline of the United Arab Emirates, is working with Taleris as part of a proactive approach to manage its fleet of Airbus and Boeing aircraft. The technology analyzes data from multiple sensors on components and systems, and warns of imminent problems. According to Werner Rothenbaecher, Etihads senior vice president/technical, With Taleris prognostics, we will be able to predict future faults and take proactive measures, which result in less unscheduled disruption to our global operations.

Analytics-powered solutions are providing insights about:

Planning operations. By evaluating millions of schedule combinations in minutes, optimizers help reduce crew costs by up to five percent, thereby boosting aircraft utilization.

Recovery. To minimize losses, a decision-support service quickly generates cost-effective solutions to rapidly recover aircraft, crew and passenger schedules.

Operations management. Up-to-the-minute situational awareness enables operators to make faster business decisions aligned with improved business outcomes.

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Supply chain analytics The sooner drilling is completed and a well begins producing, the greater the revenue potential. To optimize drilling production, the industry is moving toward a highly efficient manufacturing model. Lean Six Sigma principles can be applied to key variables to identify root causes that slow down production and logistics.

With the pressure on to reduce drilling cycle time, particularly in unconventional fields, companies have major opportunities to improve supply chain effectiveness. Analytics tools can be used to provide supplies as needed, to optimize equipment movements in the field, and to make transport and water use for fracturing more efficient. Regional contracting officeswhich include warehousing, maintenance, repair and operations functionscan lead to more tightly integrated logistics.

Decision making around E&P activities in unconventional fields, where wells are smaller and drilling is faster, is highly dynamic. Crews might have thought they needed to move a rig today, but since the anticipated drilling site will not be ready as planned, the rig might need to be moved elsewhereand associated materials also will need to be there as soon as possible. Increased reliance on data and analyticsalong with effective communication among multiple functionswill enable supply chain managers to synchronize efforts to reduce cost and support higher production.

In addition, companies are likely to adopt procurement analytics to hold down costs. Objectives include greater value through spend management, inventory optimization and contract management, along with managing risks through commodity analytics and procurement risk analytics.

Case in pointUsing analytics to gain greater value from upstream gas

The full exploitation of resource potentialbe that gas or oilis a longstanding challenge. Recent estimates indicate that total stranded gas reserves are in the order of several trillion cubic feet worldwide.6

The upstream division of a leading energy company sought new opportunities for gas valorization. The company wanted to increase synergy between gas downstream activities and international negotiation functions, to closely monitor and manage the portfolio, and to increase knowledge of gas technologies.

Using a preconfigured tool to support the analysis process, the company began a multi-year journey. It started with data assessment, drawing on diverse data inputs. These included regional management, sustainability, health and safety, drilling and exploration, development, reservoir, and external oil and gas industry providers. The assessment identified a total potential of multi-billion barrels of oil equivalent for both operated and non-operated assets.

A scoring model evaluated reservoir data, existing facilities or the infrastructure to be exploited, as well as potential gas prices. The information detailing gas assets was matched with development schemes. These schemes included traditional technologies (such as pipeline or liquified natural gas),

as well as unconventional approaches, including compressed natural gas, floating liquefied natural gas and gas to liquids. The company then considered scenarios, weighing up constraints and identifying the most suitable opportunities.

How analytics helped

Within a few months, the team identified 10 assets with strong potential, and defined field-development plans for two of them. The company applied unconventional technology (compressed natural gas transportation), combined with traditional uses (such as liquefied natural gas or pipeline). In this way, the team accelerated time to market, anticipating both reserves booking and when gas production should commence. Today, the company benefits from portfolio management analysis that helps better understand the risks and benefits of exploiting their portfolio of gas assets, and how to achieve better outcomes from a mix of conventional and unconventional gas technologies.

Talent

ProcessSystems

Asset allocation

Production enhancement

Cost effective operations

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2.2 Downstream operationsEnergy companies have been collecting and interpreting data in downstream operations for many years. At a basic level, analytics have delivered insights from sensor signals, enabling monitoring of assets and output in real time. In many instances, the implementation of analytics solutions has been undertaken on an asset basis, in many cases by a technology-savvy director of operations or engineering, rather than according to an organization-wide directive or strategy. What has been less common is analysis of data across multiple assets and, moreover, across multiple facilities.

To pursue the most promising applications, company leaders need to begin by reaching consensus on key drivers of value, from refining and petrochemical operations, through planning and scheduling, to marketing and trading.

Analytics for refining and petrochemical operationsRefining has been one of the most commercially challenging parts of the energy value chain. Consequently, efforts to optimize downstream production have been mission critical for years.

As an illustration, the Hungarian Oil & Gas Company Plc (MOL) launched a project at a refinery in Szzhalombatta, Hungary. MOL implemented the full SAP Business Objects suite, including configuring users and creating key performance indicators, dashboards and additional reports. More than 200 key performance indicators were built into the system, along with 19 dashboards and multiple reports, which automated laborious activities. Consequently, managers were able to gain access to up-to-the-minute information and had the time to make business decisions to adapt to fast-changing markets. MOL implemented the solution across other units of the Hungarian refinery, and then at a refinery in Slovakia.7

In Europe and North America, pressure to be more efficient has been greater due to competition, particularly from refiners in Asia. As a result, these refiners have been more enthusiastic adopters of analytics-powered solutions to optimize production.

Analytics for operationsThe shift to higher cost, more complex refineries creates the need to maximize return on investment. Reducing downtime and improving recovery from unplanned events is essential. Consequently, energy companies will rely on asset maintenance analytics to improve margins. It is not far-fetched to envision drones operating in commercial spaces, as a continuation of increasingly perceptive devices to monitor asset performance.

To realize additional gains, operations managers are likely to consider combining asset-maintenance analysis with improvements in associated processes. Knowledge of the analytics of rotating equipment, for example, needs to be communicated to work crews and those who manage equipment and spare parts.

Planning and scheduling activities, along with predictive asset maintenance, help to improve rates of equipment utilization. At some companies, planning and scheduling are handled at headquarters, but many mid-sized refineries have their own departments. Performance analytics can be applied to identify the deviation between plan and actual results, thereby spurring new thinking on ways to improve performance. The potential benefits include cost avoidance, improved utilization of assets and higher marginsa continuing goal for refiners.

Thinking holistically provides greater visibility into the data throughout the full cyclefrom planning and scheduling of production, to the supply chain, to demand planning, marketing and trading.

Analytics to optimize the hydrocarbon value chainThe margin squeeze continues to drive the need for operational excellence, which real-time business intelligence can help enable. In addition to the supply chain gains discussed in the upstream section, downstream organizations have opportunities to improve fuel movements and balance their portfolios for improved returns.

The increase in hydrocarbon shrinkage, through theft and leakage, is a problem that can be identified and tracked through logistics analytics. Monitoring devices on trucks collect a steady stream of data that can be used to determine when deliveries run off course.

Potential gains from supply chain optimization can be large, particularly for national oil companies with large territories, as well as many pipelines and depots. Bottlenecks, or choke points, typically occur not at the refinery, but in logistics and distribution. Therefore, the underlying data, software and analytics tools related to these processes provide opportunities for improvement. The ultimate goal is end-to-end integration, from the demand side through production, into commercial channels, enabling monitoring of the entire manufacturing value chain.

Analytics for health, safety, security and environment (HSSE)Avoidance of serious issues provides a continuing license to operate, as well as improved regard for human resources. Also, increasingly stringent rules for HSSE mean that downstream companies need more real-time monitoring, as well as timely and accurate reporting for compliance purposes.

Companies can implement trace-and-track technologies in multiple refineries, covering thousands of people at the same time. Analytics can give managers more intelligence about what is currently happening, and insights from accidents and near accidents.

Marathon Petroleum Company LP implemented a wireless-based safety solution at its refinery in Robinson, Illinois. The solution integrated Wi-Fi and location-based technologies with multi-gas detectors to allow for remote monitoring of potential incidents. In addition to the initial safety application, the industrial-mobility infrastructure has provided a foundation for analysis of additional real-time data. As a result, Marathon has opportunities to trace and track not only people in potentially dangerous situations, but also contractors and equipment utilization.

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While tracking data makes some people uneasy from a privacy perspective, many others are likely to appreciate the value given workplace risks. In addition to tracking employees and contractors, analytics can provide insight into the emission and movement of hazardous gases and materials. Analytics could contribute to improving standard HSSE operating procedures for operations and transport. Sophisticated analytics could provide insights to manage risk more precisely, enabling companies to determine multiple levels of risk and protection.

In addition, advanced analytics can detect abnormal behavior on networks, through user logins or document requests, to detect potential intrusions. Analytics-powered systems can automate defensive responses and suggest other methods to thwart cyber criminals.

Analytics for marketingOutdated technical infrastructure is unable to respond to todays digitally-enabled consumers. Highly connected consumers are better informed, price driven and harder to retain because competitive information is mere clicks away. Electronic payment mechanisms provide a new digital source of data, and companies can use that data to get to know customers better.

While some energy companies are leaving retail operations to franchisees, others may see opportunities in running analytics on thousands of retail sites, mining the data for insights into customers and envisioning new services (perhaps not petroleum related) for additional revenue streams. Using analytics to aggregate and analyze social media postings helps businesses understand customers better, thereby providing insight before retail decisions are made. Energy companies have much to learn from the retail products and banking industries, which are leaders in combining digital, mobile technologies, the cloud and predictive analytics to increase sales, improve retail operations and identify opportunities.

Analytics for trading and risk managementSome oil and gas companies already have mature analytics capabilities for trading. With highly regulated markets, companies have begun modeling, forecasting regulatory changes and gaining real-time visibility into the global commodities market. Indeed, some are applying for full trading licenses and need capabilities that rival those of a financial institution. The emphasis on innovation likely will persist as commodity volumes increase and the need to manage the related risk is directly correlated.

With greater insight into commodity markets, energy companies can manage accordingly their supply chains, for example, leveraging a fleet of energy vessels to determine if they hold onto supplies or route them to another destination. This example shows how analytics can be used across multiple processes, thereby improving business outcomes in multiple functions.

2.3 Corporate functionsAdvances in information technology during the last quarter century have increased the transparency of oil and gas operations, accelerating data collection and analysis. ERP packages have promoted standardization in areas such as finance and accounting, procurement, supply chain and human resources,8 thereby providing more reliable data for analytics.

Opportunities in the corporate space have much to do with extending reach to include upstream or downstream activities, thus increasing organization-wide visibility. Energy businesses need a modern ERP system integrated across production, revenue, owner disbursement, transportation and marketing, compliance and accruals processing.

Data consolidation provides the platform for advanced analytics and improved portfolio visibility. Companies typically begin by tackling master data management and improving data quality. Larger sets of harmonized data and advanced analytics offer rich resources and tools for statistical analysis, modeling and insight into corporate performance.

Analytics integrating corporate and operations dataAnalytics-powered systems for production revenue accounting can help companies bridge upstream operations with corporate systems. Well-integrated, highly automated systems reduce manual input and eliminate duplicative processes. As a result, companies are able to benefit from reduced time in processing prior-period adjustments, and to conduct cross-application reporting and analytics.

Energy companies are working to improve visibility into their vast and growing stores of data. Disparate systems and complex configuration of scores of customized reports are giving way to a single source of the truth, along with faster, simpler reporting. User-friendly designs provide access to the shared, up-to-date data, and convenient searching capabilities make it easier to find relevant performance indicators, and report performance for a wide range of processes and functions.

In addition, software companies are developing user-centric approaches to help people make sense of the data in visually appealing, graphical ways. Application developers are working to bridge the divide between popular mobile applications and the corporate model, which has taken a flat look at data. The advent of intuitive tools will decrease the need for extensive training and improve analytics uptake throughout the organization.

ERP systems and integrated applications can help close the missing middle that prevents high performance. (For more information, see Day in the Life sidebar).

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Case in point"Day in the life" scenario illustrates an end-to-end approach to production loss analytics The following scenario illustrates how an upstream energy company integrates data, mobile devices and analytics tools with enterprise systems. In this example, executives identify production losses, the causes of which are addressed, resolved and documented due to well-integrated digital technologies.

At an executive meeting, the results on a corporate dashboard potentially highlight production losses that affect multiple functions, including sales, finance, and health and safety. Attendees at an operations meeting discuss possible causes, which include equipment failure and liquid loading.

A field technician locates a failed compressor and, using a mobile device, captures the reason for the failure. The technician creates a work order, with analytics-powered systems suggesting optimal dates for the well workover. An in-memory database speeds the process of figuring an optimal time to run the well work.

The schedule is sent to a service company to plan crews and equipment. The upstream organization now can schedule against resources, and analytical tools suggest a timetable consistent to the business rules for the lowest cost and highest revenue.

A contractor is selected and a work order is dispatched to the mobile device of a certified technician, who performs the required operations. During the workover, the technician notices a potentially hazardous incident, and records it with minimal time and effort. He records the time spent completing the operations against the work order. All of this information is fed back to the ERP system, where cost and revenue issues are stored, analyzed and reviewed on dashboards.

The benefits of such an integrated approach can improve outcomes across multiple functions and assets:

Rapid access to integrated data speeds root-cause analysis

Improved operational insight is possible due to fast access to data and predictive analytics

Having a more complete operational picture leads to reliable planning and efficiency due to improved resource scheduling

Risk is reduced and safety enhanced through visualization of the work site.

Faster resolution of production loss not only reduces cost, but also leads to higher production and greater profitability for the energy company.

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Analytics for talent management. In the area of human capital, companies have opportunities to apply analytics to relieve chronic talent shortages. Analytics can deliver insights into hiring and retention practices, geographic locations of certain skills, the lead time to competence, training and change enablement.

Analytics for finance. In the audit area, energy companies can use larger data sets to adjust audit coverage to areas posing greater risks. This broader approach, only recently feasible and affordable, is reducing time spent on auditing by about 15 percent for one company, thereby reducing costs while delivering higher quality.9

The number of analytics applications in the corporate area is growing. Rather than limiting the scope and targeted outcomes to a specific function, organizations can improve performance by broadening the focus of analytics initiatives and sharing the data and insights gained with the owners of related processes. Next-generation business process outsourcing will see value hunters using analytics for insights to generate improved outcomes across functions and processes.

Case in pointAnalytics enable oil field services company to improve sales processes, operations and revenuesIn its annual review, the finance department of an oil field services company discovered an interesting response to the companys shift from a product-oriented approach to a focus on geographic markets. All areas of the business relied on product-specific data to help adjust pricing and sales strategies, yet the finance teams forecasting and reporting mechanisms were targeted to geographic results. From the boardroom to the oil field, the feedback on what was needed was financial facts, as well as fast and clearer visibility of customer pricing and profitabilityby product.

To address these demands, the company conducted a pilot across two product lines to aggregate customer data and gather accurate, timely information to drive more proactive decision making. Using modeling techniques and producing the results in a spreadsheet, the pilot proved to be successful. The reports gave visibility to an unprecedented granularity of data, offering unique insights that brought together essential financial statistics with previously unknown patterns of customer behavior. From district managers to the chief executive officer, this information gave a new, powerful visibility to the business that fueled even greater interest in analytics. As a result, the steering committee elected at the pilot stage to roll out the concept across three of the companys regions in the Americas and Europe.

Analytics rewards

Alongside other initiatives, such as price discipline, price books and contract reviews, the finance team furthered its use of analytics to improve the organizations revenues, enhance sales processes and gain operational benefits.

With analytics proving to be a key enabler, the finance team increased resources to help the company understand more about the broader challenges it faced. And the dynamic details are affecting different areas of the organization in diverse ways, such as changes in the sales process, where sales tendering teams are being established to manage large tenders alongside analysts working on global price books.

Aside from the financial gains from deeper analytics insights, there are non-financial, strategic and operational benefits. Support for analytics activities means a consistent and integrated approach to data across the organization, discouraging practices like the distribution of ad-hoc spreadsheets. Analytics also can help identify whether the company is compliant with data policies and master data management. Above all, the benefits of analytics have reached the hearts and minds of the companys employees. Additional roles to keep pace with the demand for data evaluation are presenting new career choices. In the United States, a team of nine people is fully dedicated to analyzing the output of the analytics model, working effectively with other areas in the business not only to review costs and trends, but also to realize additional valuesuch as realigning

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product pricing or establishing greater control around pricing disciplines. In this way, roles within the finance department have evolved beyond those typical of the function, with a team that brings together financial acumen and insights to act as a spur for business development and better serve the companys customers.

Sustainable improvements

Fully committed to using analytics, the company aims to create a central analytics team that evaluates all aspects of the organizations revenues and products.

Reaffirming the maxim that data never lies, the company has uncovered some surprising truths about the status of customers and their relationship with the energy company. As a result, employees have gained greater confidence by being able to tap into data that can be swiftly verified and substantiated, and they are actively seeking new ways to use data to drive revenue and profitability growth.

The company credits much of its success to choosing the right business partner to effectively plan and implement an analytics modeland realize results in a mere 90-day window. What is more, having a senior-level steering committee consisting of senior executives drawn from across the business meant there was a clear understanding of the value of the information they could gather. As a result, the right resources could be allocated from the outset to manage the broader impact of the program.

Making analytics matter

This companys program for enterprise analytics demonstrates the following success factors:

Running a centralized backbone of information with a single instance of SAP and Hyperion eases compatibility issues.

Senior-level support and active involvement generate a constantly growing supply of interest and engagement.

Proving out the model rapidly in the early stages leads to rapid buy-in from finance and other department leads.

Ongoing relationship- and knowledge-building with the steering committee builds enthusiasm.

Control and manageability are maintained by keeping the sales process team small with clear boundaries around the organization and charters, pilots on pricing analytics and contract harvesting.

The companys project team is excited about the future for analytics and sees demand outstripping supply for many months ahead. What is more, far from being a back-office function, the finance department has been catapulted into the limelight and now plays a pivotal role in defining and maintaining the strategic direction of the business.

Analytics have been embraced rapidly and positively across the organization, said the vice president of financial planning and analysis. We have transformed what was a latent desire for analytics into a strong commitment to using analytics to better understand our business.

Results 2013

Target 2014

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Section 3 Achieving better business outcomes from analytics in oil and gasIntegrated actionsin the areas of data, technology, process, people and cultureare needed to execute a strategy that leads organizations on a journey from issues to better business outcomes. Adopting such a holistic approach provides an opportunity to generate insights to improve performance where it matters.

Target 2016

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3.1 Accenture analytics capability maturity modelTo derive improved outcomes from analytics, the essential starting point is taking an objective look at the state of current capabilities. Data and technology are two areas that need to be assessed, but an organization-wide perspective for change also calls for process and people perspectives. Data and analytics need to be

embedded into revised job responsibilities and into workflows. Additionally, peoples attitudes toward data and analytics need to be assessed, along with talent issues and organizational change.

As oil and gas companies create their road maps to develop greater analytics capabilities, Figure 9 shows a maturity model for how organizations can chart where they are today. All of these capabilities in combination are important to become an analytics leader, where the discipline becomes embedded in an energy companys competitive essence.

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Level 1 Level 2 Level 3 Level 4 Level 5

Analytical novices Localized analytics Centralized analytics Analytical companies Analytical leaders

Strategic focus

Use of analytics on an ad-hoc basis by individuals.

Some analytical tools, but not widely used nor understood, and tending to be used in discrete functions.

Shared toolset used for strategic decision making. Varied uses by function, primarily in support of KPIs and corporate dashboards.

Comprehensive toolset and broad functional use for strategic, tactical and operational decision making. Recognition of data and process owners.

Organization-wide use. Scenarios and predictive analytics shape and influence operational and strategic decisions.

Data Poor quality or missing data. Data is predominantly internal and structured in nature. Many different types of data marts by asset.

Asset-based data model (the data is integrated at asset-level, but not across assets or the enterprise). OT and IT data are distinct. Data is collected on daily basis.

Structured data exists in a centralized environment. Some intra-day data availability. Evolution of enterprise data model focused on delivering value.

Starting to combine structured and unstructured data. Using rapid data discovery capabilities to visualize and analyze large volumes of data. OT and IT data are well integrated. Data is enriched through third-party sources.

Data models conform across OT and IT. Strong capability to store and analyze structured and unstructured data, generated internally and externally, at speed. Collection methods designed into technology and processes.

Technology Reliance on spreadsheets and local databases.

Disparate data warehouses and isolated databases. OT and IT work in silos; integration tends to be done manually.

Big data technology reveals insights from multi-structured, high-volume and high-velocity data. This activity identifies areas with high value that can be promoted into a more traditional analytic data warehouse, resulting in a hybrid technology landscape. OT and IT systems begin to converge.

The hybrid landscape matures and combines big data with a traditional data warehouse. Introduction of a service framework around the data to support mobile and web apps. Adoption of more sophisticated tools for visualization.

An integrated data-service platform from which business-specific applications are developed. Landscape includes in-memory platform. Fully integrated web apps. Ability to store low-level data for longer to support deeper analytics.

Process Basic analytics used primarily to improve existing processes.Lack of understanding about how analytics can provide insights that will change processes.

Some statistical analysis conducted (e.g., Monte Carlo simulations), as well as risk and variance analyses. Efforts are, however, limited to specific activities rather than focusing on end-to-end processes.

Scope broadens, with centralization of data cleansing, charting and mapping of KPIs at a high level. Implementation of advanced analytics, however, is lacking, and much of work is done in realm of business intelligence.

Rigorous approach to process improvement using advanced analytics. Certain processes are well mapped and enable cross-functional collaboration. However, activities lack integration and are not consistent across business units. Issues are perceived as unique and thus data and best practices are not widely shared.

Advanced analytics are rigorously built into end-to-end processes to help companies compete more effectively. Analytics activities are integrated and consistent across business units.

Organization No clear ownership of data. Inconsistent and variable data points.

Efforts are siloed, with data ownership by function and limited to business intelligence. Data classification scheme lacks sophistication. KPIs typically are limited to a single data source. Delivery models are inflexible.

Development of governance structure, and roles and responsibilities for managing and analyzing data. Data reference model and management processes are defined. The analytics toolset facilitates key organizational metrics. Analytics skills are pooled and insights produced for organizational benefit.

Data across processes is owned with clear accountability. Rewards and recognition systems are in place. Information leads to continuous improvement of business processes, paying off in improved margins. Chief data officer (or similar position) is filled. Business insights from data are understood and decision making is based on reference analytics. Analytics think tanks focus on key business challenges.

Organizational capability to develop and interpret analytical models. Data sources extend beyond traditional back-office applications to support improved EH&S, maintenance and other areas. Analytics shape organizational processes. Analytics-driven outcomes create a differentiated proposition, competitive advantage and improved market share.

Talent Analytics skills are limited to the talent within the existing workforce.

Analytics is being built into role definitions and job descriptions. Capability development tends to be on an as-required basis, and not perceived as a core competency.

Broad reference to analytics across job functions and levels. Training of people on how to use analytical tools. Analytical skills are valued and more experienced people with specific capabilities are recruited.

Analytics is measured as part of the performance process. Specific roles are defined to develop and maintain the integrity of data. The ability to interpret analytical data is broad across the organization.

Analytical capabilities pervade the business. Critical business processes have dedicated data and analytical owners. Insight, based on analytics are embedded in processes, cultivating development of new products and services.

Culture and Performance Management

Analytics skills and capabilities are not recognized.

Reward and recognition processes for analytical capabilities vary throughout the organization and are valued inconsistently.

Analytical competence is encouraged through the performance management process. Training curricula are aligned to individual development and analytical competence. The right metrics are tracked, and data is relied upon to deliver insights and lead to better business decisions.

Individual contributions to KPIs are well understood, and people see how data leads to actionable insights. Specific performance factors are associated with analytical competency. People are accountable for the integrity of data they manage. Leadership displays analytical competence and outcomes are celebrated.

Performance factors encourage broad use of analytics. Analytic performance is inherent in all job descriptions through all levels of the organization. New business ideas based on analytical insight are rewarded. A high level of maturity attracts experienced analytics talent.

Figure 10. Stages of analytics maturity: The Accenture Maturity Model for oil and gas companies.

Source: Accenture Analytics, 2013.

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1. Visualize the value and design for analytics outcomes Ask the right questions

Develop a value-led road map

Identify gaps and work diligently to close them

Help the business visualize the value at speed

2. Adopt an end-to-end process view, integrating enterprise and operations analytics Define an organization-wide analytics strategy and communicate

Close the loop defining the right metrics, across functions for insights that lead to action plans

Leverage existing investments while embracing big data platforms

3. Promote a cultural shift to an analytically astute, insight-driven enterprise Ensure leadership leads by example

Select the right analytics operating model

Get the most from the talent the company employs

Attract, develop and retain analytics talent

Foster new behaviors with a change enablement program

Three recommendations for becoming analytically-powered

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Analytics is a vital resourcean increasingly valuable component of an information value chain, where applications serve not only specific users and functions, but also contribute to optimal business performance.

3.2 Recommendations for becoming analytically-powered

Recommendation #1: Visualize the value and design for analytics outcomes Analytics have been a challenge, partly because companies have tended to conduct the process in exploratory ways. Data has been collected and analyzed, rather than identifying upfront the data most likely to further the realization of specific business objectives.

Business leaders need to think outside of the box, applying analytics across processes, functions and assets, for initiatives likely to deliver the greatest value.

Ask the right questions. It is easy for employees to get lost in the detail and become distracted. As data proliferates, the ability to identify relevant conclusions becomes more challenging. Consequently, though seeming obvious, the task of asking the right questions is far from trivial and goes counter to traditional business intelligence methods.

Develop a value-led data road map. Oil and gas organizations can develop more mature analytics capabilities while being mindful of technology cost and disruptions. Existing data warehouses and business integration platforms can be integrated with complementary analytics technologies

in a holistic way. When assessing analytics products from vendors, look for new technologies that are built to work with IT systems in place.

As mentioned earlier in this report, one of the stages in the road map for many oil and gas companies will be improving master data management and data quality. Leading vendors provide an integration layer to format structured and unstructured data, providing a foundation for big data analytics. For the long term, building an integrated, smart data service platform can ease the development of business-specific applications and services. This platform will make it easier to integrate IT and OT data, with machines learning continuously from data inputs and automatically altering rules to produce improved outcomes.

Identify gaps and work diligently to close them. Data gaps correspond to missed opportunities. The challenge is to determine not only how to collect the right data, but also, in many cases, to identify data that is needed and, if missing, how to generate it.10

Catalog new data sourcesinternal and externalto begin to fill in missing information. Develop a data creation strategy to obtain data by setting up additional sources, including machine-to-machine, new software and data from business partners.

Compared to the current generation of software, which was designed for functionality, the next generation is likely to be designed to enable organization-wide analytics. Applications will be able to capture required data, with user interfaces updated to obtain new pieces of data. In the software procurement process, data collection presents a new set of requirements.

Help the business visualize the value at speed. Big data platforms enable energy companies to bring together vast quantities of structured and unstructured data. This powerful computing capability is complemented by refined visualization

technologies that speed data discovery, thereby shortening the time to evaluate what is being communicated and to make decisions. Energy companies would be able to deliver insight in weeks as opposed to months, or even years, and pursue an iterative approach to modeling. Secondarily, compared to previous approaches, this approach creates a more dynamic experience of data-driven change. It can cultivate confidence from people in the business, thereby encouraging greater adoption. Nearly all of the leading analytics vendors have corresponding data visualization solutions that make sense of complex data.11

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Recommendation #2: Adopt an end-to-end process view, integrating enterprise and operations analyticsGiven the relative immaturity of analytics as a discipline, especially predictive analytics, it is hardly surprising that most energy organizations have yet to reach the point of organization-wide adoption. However, energy companies so far have implemented analytics simultaneously, both strategically and tactically, driven by corporate strategies or field-level technical needs. The integration gap, or missing middle, is compounding dissatisfaction with analytics-driven investments.

The gap can be characterized by the data integration challenge between IT and OT, and by the lack of collaboration and transparency between operations and enterprise analytics initiatives. Whether the missing middle pertains to data or technology gaps, or lack of collaboration among people and groups, in all instances the common element is a break in end-to-end processes (see Figure 11).

Energy companies need to identify disconnects between operations and the back office, and work to integrate operations technology with information technology.

Define an organization-wide analytics strategyand communicate. Energy companies stand to gain most by developing and acting on an organization-wide, integrated analytics strategy. Such a plan needs to mobilize IT knowledge for data; analytics and modeling experience for analytics; and deep industry knowledge to interrogate the resulting insight and take action for improved outcomes.

A holistic approach can be visualized in the context of end-to-end workflows. When processes are viewed as parts of a life cycle, it is easier to see how data and analytics are important to multiple functions and assets.

Figure 11. Adoption of analytics has been approached either at the corporate or the field level.

Source: "Landscape of Analytics in the Oil and Gas Industry," 10EQS, 2013.

Field-led analyticsAd-hoc or business-unit adoption is driven primarily by the need to overcome technical pain points in operation.Bottom-up approach results in high data availability but with limited integration.

Corporate-led analyticsCorporate initiatives are usually driven by internal IT, and by operations technology strategy and teams. Top-down approach targets system integration but lacks domain-level understanding, which affects data availability.

Field vs Corporate

How the digital oil field is implemented depends largely on the culture of the company. Some start either at the top or bottom, and neither approach works that well in isolation. Ideally it needs to be both together. Engineers and management speak different languages. They do not understand each others data and data needs. The key to overcoming the adoption issues in digital oil field technology is by addressing the missing middle.

Former head of the digital oil field for an integrated major oil company12

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Close the loop. By routinely defining targets and assessing the outcomes achieved from analytics, energy companies can make course corrections in a closed-learning-loop process (see Figure 12). Thanks to the virtuous feedback loop, energy companies can revisit questions to compare new data and insights against changing business conditions and strategies.

If analytics do not produce the expected results, it is helpful to look for root causes. Three common pitfalls, with suggestions for overcoming them include:

1. Measuring the wrong metrics. Energy companies need to identify the right metrics and bridge gaps in measurement methods.

2. Flawed insights. Users need to identify and validate across functions to develop valuable insights and take action.

3. Faulty execution. Linking analytics capabilities to business outcomes, insights need to be embedded in key decision processes throughout the organization.

Any analytics strategy needs to consider how people execute on the business insight. Processes and technologies may need to be redesigned to improve integration, and change enablement is a concern (see recommendation #3). Engineers and geologists, for example, who tend to be protective of their data, may need communications and training to be convinced that sharing information will result in improved business outcomes for the organization as a whole.

Leverage investments while embracing big data platforms. Energy companies have invested heavily in the technology backbone: enterprise resource planning systems, data storage and integration tools, as well as point solutions for analytics. As returns from traditional business intelligence platforms diminish, a hybrid technical landscape is needed to accommodate evolving data requirements and enable an analytics-powered strategy.

Big data platforms provide a mechanism to integrate unstructured and structured data, linking OT with IT, thereby responding to one of the energy industrys biggest integration challenges and offering improved visibility into end-to-end processes.

By taking steps to close the missing middle, oil and gas companies can move closer to the goal of being insight driven from end to end. Businesses that systematically use data at all levels of the organization are able to make more intelligent decisions.

Figure 12. Closing the loop: Embedding integrated analytics capabilities into decision making processes.

Identification of Key Value Drivers

Business Review Cycles

Questions on Key Metrics

Insight Generation

Value Realization

ExecutionInsight

Validation

Sample Outcomes Optimize safety

investments Improve asset use Spend capital

efficiently

Sample Analytics Equipment safety monitoring Maintenance reliability analytics New ventures investment and risk

optimization analytics

Examples of Issues1. Health and safety 2. Commercial

and production optimization

3. Capital delivery

Operations Management Analytics

Commercial Management Analytics

Enterprise Management Analytics

BI and Packaged Workbench

Technology Enablers

Analytics Center Of ExcellenceCore Analytics

Functional Analytics

Cross-functional Analytics


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Recommendation #3: Promote a cultural shift to an analytically astute, insight-driven enterpriseAnalytics are valuable only as far as people in the organization know how to act on the insights. Consequently, effective application of advanced analytics may require changing business behaviors by focusing on habits, mindsets and processes.

Energy companies are likely to benefit by evolving their organizational cultures to be more analytically orientated. Knowledge sharing and collaboration, for example, would need to increase for the realization of greater benefits. Processes may need to be redesigned to reflect end-to-end workflows. Data and outcome ownership also would be essential for taking action based on analytics-powered insight.

Designing an analytically astute organization calls for blurring the lines between business functions (as consumers of business data) and IT (data purveyors), thereby fostering a culture of collaboration. Corporate developers would need to focus on incorporating methods of data harvesting into user-interface designs, making data collection apparent yet unobtrusive to users.

The pace of organizational action needs to keep up with the speed of data-driven insights on market opportunities. Oil and gas organizations can take several actions to promote integration of insight-to-action processes:

Ensure senior executives lead by example. Business leaders need to adopt and encourage uptake of analytics as a mindset. Senior leaders would do well to set an example by calling for basing their decisions on data-driven analysis, as well as from business experience.

An organizational role such as a chief data officer can help bridge information gaps among functions and levels, and promote analytics as the source of improved outcomes. By creating such a role, organizations typically are able to provide ownership for key analytical-related requirements, such as data definition, data taxonomy and data architectures. Data champions in each of the business units can encourage others to find ways to collect data that is up-to-date and high in quality.

The oil industry is conservative and slow to change. The command-and-control organizational structures and siloed working conditions do not promote the collaborative working environment that analytics enables.

CEO with 10 years of experience in analytics12

Figure 13. Total shortages and surpluses of analytics talent by country, 2010-2015.

Shortages of analytics talent Surpluses of analytics talent

Source: Analytics in Action: Breakthroughs and Barriers on the Journey to ROI, Accenture, 2013.

The shortfall of analysts in the US will exceed the surpluses expected in India and China combined.

Data is for all types of analytics talent across all industries within each country

250,000

200,000

150,000

100,000

50,000

0

-50,000

-100,000

-150,000

-200,000

-250,000

-300,000 US Brazil UK Japan Singapore China India

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Select the right anlaytics operating model. Oil and gas companies likely will adopt and evolve different operating (and consequently organizational) models with the maturation of their analytics capabilities. A centralized model, for example, makes sense as the organization is establishing governance, roles and responsibilities, and processes for managing and analyzing data. In the centralized model, analytics skills are pooled to produce insights for the organization at large, as well as to promote analytics excellence. In a more mature talent model, experience might become more decentralized, as all personnel are held accountable for the integrity of data they manage, and measured on how far they act on the resulting insights.

Attract, develop and retain analytics talent. One of the greatest challenges to driving an analytics mindset is the shortage of analytics talent. The constraints on the talent pool reach across industries throughout the world.13 Research from the Accenture Institute for High Performance shows a global mismatch. A recent study analyzed job growth across all industries in seven countries and found a critical mismatch between supply and demand (see Figure 13).

Industry efforts have led to more people obtaining degrees in petroleum engineering, but the shortage remains large. Accenture projects the United States could encounter a sizable shortfall of more than 260,000 analysts by 2015. In other countries, however, a surplus of analytics skillsup to 175,000 analysts in Indiais likely. In addition to a shortage of analytics experience, employers have additional skills requirements, such as competency in particular languages or familiarity with the basics of business operations, making the search for talent even harder.14

Get the most from the talent you employ. Many organizations have untapped analytical res

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